Method of conducting social network application operations

ABSTRACT

In one embodiment, a method of sharing locations of users participating in a social networking service at a geographic location, the method executed by a computer system and comprises: receiving location information and text descriptive information from a mobile device of a first user of the social networking service, the location information representing a geographic location of the first user, the text descriptive information manually provided by the first user on an input module of the mobile device; associating the location information with the text descriptive information of the first user in a database; sending the text descriptive information and the location information of the first user to a second user for display.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.13/586,839, filed Aug. 15, 2012, which is incorporated by reference.

BACKGROUND

Location based services refer generally to services that provideinformation to a user in relation to the location of the user. Manyprior location based services are relatively pedestrian in nature andprovide relatively simple information. An example of a known locationbased service is a “weather” service in which the user's zip code isprovided to the service (e.g., through a conventional HTML webpage, aWAP or other cellular phone interface, etc.) through a network and theservice responds by communicating the current weather conditions and theforecast for several days. Other known location based services provide“social” applications such as allowing users to determine each other'slocations, receive notification when a friend comes within apredetermined distance, and similar operations. Another type of locationbased services are generally referred to as “McDonalds finders” thatprovide search results in a map form (e.g., searching for specificlocations of restaurants/stores within a given distance of the user).Other location based services have proposed delivering various types of“advertising” (e.g., when a user arrives at an airport, various ads canbe delivered to the user's cellular phone). However, many such prioradvertising location based services are quite simplistic and do notpossess any appreciable intelligence for selecting advertisements beyondthe location of the user.

SUMMARY

In one embodiment, a method of conducting operations for a socialnetwork application, comprises: generating a notification list of recentactivities of users of the social network application, wherein thenotification list includes (1) at least one activity within the socialnetwork application of a first user and (2) at least one hyperlink to anoffer involving an activity that is directly related to at least oneactivity of the first user, wherein an account of the first user definesat least one notification rule for controlling visibility of the atleast one activity to other users of the social network application; andproviding the notification list to a second user, that is a friend ofthe first user within the social network application, according to theat least one notification rule of the first user.

In one embodiment, a method of providing a location based service (LBS),comprises: (i) receiving location information over a period of time byone or more software programs from a plurality of wireless devicesbelonging to a plurality of subscribers; (ii) processing the locationinformation to detect that respective subscribers tend to spend time atone or more locations with one or more other specific subscribers; (iii)storing data indicative of a tendency of each such subscriber to spendtime with the subscriber's one or more other specific subscribers; (iv)detecting whether subscribers are present at locations with one or morespecific subscribers identified in the stored data subsequent toperformance of (ii) and (iii); and (v) comparing ad parameters againstsubscriber data, to select ads for communication to subscribers, whereinthe comparing differentiates in selection of ads for communication tosubscribers in response to (iv).

In another embodiment, the activities identified in the logs are definedin a hierarchical manner. In another embodiment, the logs identify whenan activity has been completed. In another embodiment, the logs indicatecompletion of financial transactions with merchants.

In one embodiment, the server for LBS services comprises: one ormultiple databases storing information identifying subscribers of one orseveral LBS or other applications, wherein the one or multiple databasesidentifies groups of subscribers that have been detected to be locatedin close physical proximity on multiple occasions; one or multipledatabases for storing advertisements to subscribers; code fordetermining whether subscribers are currently clustering based uponlocation information pertaining to subscribers; and code for selectingand communicating advertisements to subscribers based on locations ofthe subscribers, wherein the code for selecting and communicatingdetermines selects ads for subscribers depending upon whethersubscribers have been determined to be clustering.

In another embodiment, a method comprises the operations performed bythe one or more first programs, by the one or more second programs,and/or the LBS applications.

In another embodiment, a method of providing a location based service(LBS), comprises: receiving location information by one or more softwareprograms from a plurality of wireless devices belonging to a pluralityof subscribers of one or more location based services; processing thelocation information, by one or more software programs, to identifyactivity of subscribers at merchant locations; maintaining a respectiveprofile, by one or more software programs, for each of the plurality ofsubscribers that reflects norm shopping activity for the respectivesubscriber; comparing information pertaining to current or recentshopping activity, by one or more software programs, for each subscriberof the plurality of subscribers against information stored in theprofile of the respective subscriber; selecting ads, by one or moresoftware programs, for each subscriber of the plurality of subscribersin relation to the comparing; and communicating, by one or more softwareprograms, the selected ads to plurality of wireless devices belonging tothe plurality of subscribers.

The selects ads can be communicate to wireless devices while theplurality subscribers are conducting current shopping activity. Inanother embodiment, each profile comprises one or more activity normparameters for a plurality of merchant types.

In another embodiment, a method communicates information to users of asocial network application. The method comprising: operating at leastone social network application server for interacting with users of thesocial network application, wherein at least some of the users of thesocial network application are users of wireless subscriber devices;providing a first mobile application for interacting with the socialnetwork application, wherein (i) the first mobile application is furtheroperable to employ geolocation functions of a respective wireless deviceto communicate geolocation information to one or more servers ofhardware and software of the social network application, (ii) the firstmobile application is operable to automatically to upload photos to arespective user account with text-limited descriptive informationmanually entered by the respective user for posting on a webpage of thesocial network application for the respective user, and (iii) at leastsome entries of text-limited descriptive information communicated fromthe first mobile application and received by the one or more servers ofthe social network application are indicative of current activities ofrespective subscribers; logging activities of users of the socialnetwork application using at least information received from the firstmobile application, wherein the logged activities include real-worldactivities of users of the social network application; receiving appusage information from a plurality of second mobile applications ofdifferent types by one or more servers of hardware and software, wherein(i) each of the plurality of second mobile applications is (a) differentfrom a web browser application and (b) different from a social networkapplication, (ii) the plurality of second mobile applications include atleast mobile gaming applications, mobile digital content applications,and shopping-related mobile applications, and (iii) the second pluralityof mobile applications are functionally integrated with the socialnetwork application to share subscriber activities across the firstmobile application and the second plurality of applications; combiningapp usage information from the second plurality of mobile applicationsand social network application activities to create combined activitylistings of user activities for respective users including informationfrom the first mobile application and information related to usage ofthe plurality of second mobile applications; presenting the combinedactivity listings to viewing users in respective webpages by one or moreweb servers; selecting ads according to ad parameters for distributionto users of the social network application, wherein the selected ads areselected, at least in part, using app usage information; andcommunicating the selected ads to users of the social networkapplication.

The foregoing has outlined rather broadly certain features and/ortechnical advantages in order that the detailed description that followsmay be better understood. Additional features and/or advantages will bedescribed hereinafter which form the subject of the claims. It should beappreciated by those skilled in the art that the conception and specificembodiment disclosed may be readily utilized as a basis for modifying ordesigning other structures for carrying out the same purposes. It shouldalso be realized by those skilled in the art that such equivalentconstructions do not depart from the spirit and scope of the appendedclaims. The novel features, both as to organization and method ofoperation, together with further objects and advantages will be betterunderstood from the following description when considered in connectionwith the accompanying figures. It is to be expressly understood,however, that each of the figures is provided for the purpose ofillustration and description only and is not intended as a definition ofthe limits of the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a system in which multiple LBS applications and otherapplications provide location based services to multiple subscribers andin which advertisements can be directed to the multiple subscribersusing multiple types of information according to one representativeembodiment.

FIG. 2 depicts a block diagram of a subscriber device adapted fordelivery of advertisements according to one representative embodiment.

FIG. 3 depicts a flowchart for identifying clustering according to onerepresentative embodiment.

FIG. 4 depicts a flowchart for processing cluster data according to onerepresentative embodiment.

FIG. 5 depicts a flowchart for utilizing cluster information accordingto one representative embodiment.

FIG. 6 depicts a flowchart for utilizing cluster information accordingto another representative embodiment.

FIGS. 7-14 depict activity norm summary information that can be compiledor calculated for use in selecting ads to communicate to subscribersaccording to some representative embodiments.

FIGS. 15-17 depict respective flowcharts for processing activityinformation and/or financial transaction information to generaterespective norm parameters for storage in subscriber profiles accordingto some representative embodiments.

FIGS. 18 and 19 depict activity norm profiles and for different merchanttypes according to some representative embodiments.

FIG. 20 depicts activity norm analysis that cross-correlates selectedfinancial transaction behavior to other subscriber behavior.

FIG. 21 depicts activity norm analysis that cross-correlates selectedactivities and/or sub-activities to financial transactions according toone representative embodiment.

FIG. 22 depicts a system for supporting a social network applicationaccording to one representative embodiment.

FIG. 23 depicts a wireless, telephony subscriber device according to onerepresentative embodiment.

FIG. 24 depicts a user interface for use in a social network applicationaccording to one representative embodiment.

FIG. 25 depicts a user interface for use in a social network applicationaccording to one representative embodiment.

FIG. 26 depicts a map interface for displaying mobile subscriberanalytics according to one representative embodiment.

DETAILED DESCRIPTION

Some representative embodiments are directed to systems and methods formonitoring data associated with users of location based services anddirecting advertisements to the users. Some representative embodimentsare directed to distribution of mobile applications. Some representativeembodiments are directed to generating, processing, and/or usingbehavioral analytics. Some representative embodiments are directed tocommunicating ads for presentation to mobile devices and/or staticprocessor based systems. Some representative embodiments are directed tosocial network applications.

Referring now to the drawings, FIG. 1 depicts a system in which multipleLBS applications and other application servers 101 (also referred toherein as “applications,” “mobile applications,” or “mobile apps” forconvenience depending upon context) are provided for communication withmultiple subscriber devices 104 and in which advertisements can bedirected to the multiple subscribers 104 using multiple types ofinformation. Applications 101 may include one or more social networkapplications such as the social network applications described in theAPPENDICES of PCT Publication WO 2008/082794 A2. Applications 101 mayinclude one or more search applications such as the search applicationsdescribed in the APPENDICES of PCT Publication WO 2008/082794 A2.

As shown in FIG. 1, there are preferably a plurality of applications 101that provide location based services or other services to subscriberdevices 104. Applications 101 can provide conventional location basedservices such as map/navigation services, weather services, localmerchant search services, etc. Applications 101 can further includefinancial or shopping location based services as described in U.S.Provisional Patent Application No. 60/736,252, filed Nov. 14, 2005,60/759,303, filed Jan. 17, 2006 and 60/773,852, filed Feb. 16, 2006,which are all incorporated herein by reference in their entirety.Applications 101 can include “social” applications or gamingapplications that facilitate different types of subscriber interaction.LBS applications 101 may receive location information that is indicativeof the current location of subscribers 104 and communicate LBSinformation to the subscribers 104 according to the location informationeither upon request by the subscribers 104 or automatically dependingupon the nature/purpose of the particular LBS application 101. Theapplication data is possibly communicated through Internet 102 and awireless network 103 (e.g., a cellular network) to subscribers 104. Thesubscriber devices 104 can be any type of suitable wireless device(e.g., cellular phones, “smartphones,” wireless e-mail devices, wirelesscapable PDAs, etc.) that possess the ability to determine theirapproximate current location or communicate through a network thatenables the approximate location to be determined.

Applications 101 may also communicate with gateway/web service 105. Inpreferred embodiments, subscriber devices 104 communicate their currentlocation to gateway/web service 105. Also, as subscriber devices 104access various LBS applications 101, subscriber devices 104 communicatetheir activation of an LBS or other application to gateway/web service105. Such location and device use data may be employed for the selectionof ads for presentation to users of subscriber devices 104 as discussedherein. Gateway/web service 105 then may intermediate communicationbetween the selected application(s) 101 and the respective subscriberdevices 104. Thus, subscribers 104 may access multiple applicationsthrough the same source (or may coordinate selected communicationthrough a common server or service). Also, subscribers 104 may only needto communicate their current location to the same destination which isthen available to any application 101 as appropriate.

Although gateway/web service 105 provides such gateway services, thegateway services are not critical to all embodiments. In someembodiments, subscriber devices 104 and/or applications 101 can reportto web service 105 (i) the current location of subscribers 104 to webservice 105 as subscribers 104 utilize their respective applications and(ii) when a respective subscriber 104 accesses an application and ceasesuse of the application (if applicable).

Gateway/web service 105 updates and/or maintains a log of locationswhere subscribers 104 visited in DB 107. Also, gateway/web service 105maintains a log of interaction with or access to particular LBS or otherapplications 101. Any suitable app usage information may be communicatedto gateway/web service 105 such as transactions, app-specific actions,social interactions facilitated through one or more apps, etc.Additionally, gateway/web service 105 may maintain a log of financialtransactions completed by various subscribers as identified by financialapplications 101 (e.g., a budget application, a fraud monitoring LBSapplication, etc.) and communicated to gateway/web service 105.

Gateway/web service 105 utilizes the location information, applicationinteraction information, and/or financial information to infer theactivities performed by the subscribers and the current activity beingperformed by the subscribers. The logs of activities for subscribers andcurrent activities being performed are stored in DB 107. The logs ofactivities enable more accurate selection of ads, incentives, offers,etc. to be directed to subscribers as will be discussed below. In someembodiments, the log activities are formed into one or more listings(possibility in sequential time order) for viewing by viewing users inwebpages via one or more webserver. The viewing users may beadvertisers. Alternatively, the viewing users may be “friends” of agiven user in a social network application.

In some embodiments, the following activities and sub-activities aredefined: (i) commuting; (ii) work; (iii) school; (iv) dining—(a) fastfood; (b) casual; . . . fine dining; (v) entertainment—(a) movie; (b)music venue; . . . bar; (vi) sports/recreation—(a) health club; (b)golf; (c) athletic complex/fields; . . . gaming; (vii) shopping—(a)groceries; (b) gas; (c) clothing, shoes, accessories; (d) homedecoration; (e) home improvement; . . . sports equipment; (viii) social;(ix) traveling/vacation, etc. Of course, these activities andsub-activities are by way of example and any other activities and/orsub-activities could be additionally or alternatively employed. Also, itshall be appreciated that the activities need not be mutually exclusivein that a single subscriber could be engaged in multiple activities atthe same time. The information can be encoded in any suitable ontology.For example, a hierarchical classification of the types of locationscould be formulated. In one embodiment, specific merchants are definedwithin the hierarchical framework within shopping related activities. Anexample branch in such a hierarchical framework could be RETAIL:shopping: big-box store: TARGET®: grocery section. In alternativeembodiments, any such hierarchical descriptors assignable to locationsthat indicate the nature of the activity being undertaken by asubscriber may be employed. In some embodiments, many of the activitiesand sub-activities are related to activities at physical locations(e.g., specific locations, specific merchants, etc.).

In some embodiments, a current activity of a subscriber can be inferredfrom the type of application that the subscriber is accessing. Forexample, if a subscriber is utilizing a navigation LBS application andthe subscriber has not reached their destination, it may be inferredthat the subscriber is commuting. If a subscriber is utilizing a socialapplication, certain activities (work, school, etc.) can be eliminatedor considered less probably or relevant while other activities canpossess a greater probability (e.g., dining, entertainment, etc.).Accordingly, when gateway/web service 105 attempts to infer the currentactivity of a subscriber, gateway/web service 105 identifies theapplications that are currently active for the subscriber.

In some embodiments, gateway/web service 105 utilizes information in DB106 to infer the activity of the user. In general, DB 106 correlatesspecific locations to one or several specific activities. For example,DB 106 can be constructed by “mapping” the addresses or coordinates ofresidential areas, retail districts, schools, health care facilities,sports/athletic facilities, etc. to the particular activities that arecustomary to those types of locations. Additionally, DB 106 includesinformation at several geospatial “resolution” levels. In someembodiments, DB 106 comprises geo-coordinates or other spatialinformation that define (i) various retail districts at a higher level,(ii) specific malls, strip-malls, stand-alone stores, etc. within aretail district, (iii) specific stores; and (iv) sub-store locations. Insub-store locations, the specific goods or specific service provided canbe identified. By maintaining a log of the locations visited by asubscriber and the amount of time spent at the locations, the activitiesof a user can be estimated. Sub-store locations can be determinedutilizing any number of mechanisms and/or algorithms. For example, a GPSreceiver could be employed provided the GPS receiver possessessufficient antenna gain and sufficient reception within the store.Alternatively, many retail locations utilize multiple WiFi accesspoints. The particular ID's of the WiFi access points that aredetectable and/or the relative signal strength of the WiFi access pointscan be utilized for an intra-store location determination. Also,sub-store locating functionality can be utilized to ascertain whether asubscriber has made a purchase at a particular merchant. For example, ifa subscriber has spent an amount of time near a location where acash-register is known to be present and the subscriber leaves the storeafter being at that location, it may be inferred that the subscriber hasmade a purchase at that store.

Financial information captured by financial related LBS applications 101can be used to augment the identification of subscriber activities. Insuch financial related applications 101, the applications monitor useraccounts for the completion of transactions (e.g., credit or debit cardtransactions). Using the merchant information (merchant ID, merchantname, merchant classification, etc.) in the transaction information, theactivity can be more closely estimated. For example, if a user islocated within a mall and the user previously purchased items at aclothing store, the specific current shopping activity can be inferredto include clothing shopping even though the user may temporarily departfrom stores containing such items. Alternatively, transactioninformation may signal that a particular activity has been completed bythe user. For example, if a user makes a purchase at a grocery storethat is typical for their weekly grocery purchases, one can concludethat the user will not be conducting further significant groceryshopping for some amount of time. Transaction information can beobtained using the systems disclosed in APPENDIX A of PCT Publication WO2008/082794 A2.

As an example of a user log could be given by: 6:00 am-8:30 am:undefined; 8:30 am-9:00 am: banking; 9:15 am-10:20 am: grocery shopping;12:15 pm-1:00 pm; dining (fast food); 1:30 pm-2:00 pm: commuting: 2:00pm: begin shopping (clothing): clothing purchase made at 2:35 at youngwomen's depart. of dept. store retailer. In some representativeembodiments, logs can be reviewed by advertisers. The viewing ofactivities or selected portions thereof may be conditioned uponsubscriber privacy preferences. In alternative embodiments, the logs arenot actually reviewable by advertisers. Instead, the logs are merelymaintained in DB 107 and advertising parameters are compared against theinformation in the logs to direct advertisements.

By providing such a log of activities (previously performed andcurrently performed), a more intelligent selection of ads forcommunication to the subscriber can occur. For example, when the groceryshopping activity has been completed, selection of ads for specificgrocery items will have relatively little value. Depending upon thepurchasing behavior of the subscriber, it may be advantageous to sendthe subscriber clothing-related advertisements while the subscriber isclothing shopping (even after one or several purchases have been made).Alternatively, if the subscriber has already spent more than thesubscriber usually spends as reflected in the prior purchases, it maynot be advantageous to send more clothing advertisements since thesubscriber may have already spent their limit and is only currently“browsing,” i.e. the probability of further purchases can be estimatedas being low.

In some embodiments, activity norms and financial norms (e.g., selectedbehavioral analytics) are calculated by observing subscriber behaviorover a period of time. For the purpose of this application, the term“norm” parameter refers to a parameter that is indicative of a generallevel or average for the particular subscriber. For example, for groceryshopping norms, the typical period between grocery shopping (e.g., indays), the typical day(s) for grocery shopping, the average amountspent, the range of amounts spent, the standard deviation of amountsspent, the type of stores in which grocery shopping occurs, etc. can becompiled from location information and financial information obtained byLBS applications. As another example, for clothing-type shopping norms,the typical day(s) for shopping, the average amount spent, the standarddeviation of amounts spent, the number of transactions per shoppingevent, the average amount of time spent shopping at a particular retaillocation and/or per day, the types of stores visited, etc. can becompiled, the types of items purchases (if known), etc. can be compiled.Also, correlation between activities can be compiled. For example, itmay be observed that a particular subscriber may routinely engage in a“dining (fast food)” activity after engaging in “recreation—sportscomplex” activity. Subscriber activity and/or financial norms are thencompared against subscriber's more recent activities for the purpose ofad selection.

By compiling such information, intelligent selection of ads forsubscribers may occur. In preferred embodiments, advertisers upload adsinto ad DB 109 through ad server 108. Also, the advertisers specify anyspecific ad parameters for association with their various ads. The adparameters are compared by ad server 108 against subscriber informationand the activity information and norm information in DB 107. When theinformation in DB 107 satisfies the ad parameters for particularsubscribers, the respective ad(s) are communicated to those subscribersby ad server 108. Any other ad selection criteria can be employed. Forexample, the ads of certain advertisers can be prioritized based uponpurchased ad placements. The payment for placement of ads may includepayments to prioritize ad placement according to any ad parameterdiscussed herein or in the APPENDICES of PCT Publication WO 2008/082794A2. For example, payments for ad placements may occur according topurchasing norm parameters, clustering parameters, shopping timingparameters, etc. which are discussed below. The payments for adplacements may also utilize any such parameters in combination or incombination with other subscriber data. For example, an advertiser maypay for ad placements for subscribers that typically spend greater than$200 per shopping trip, shop at a specific type of retail establishment,and that fit a given demographic profile. All such combination of adparameters are contemplated according to some representativeembodiments.

Referring now to FIG. 2, subscriber device 200 is shown that is adaptedfor delivery of advertisements according to one representativeembodiment. Subscriber device 200 can be any suitable wireless device,such as a cellular phone, that is capable of executing softwareinstructions. The software instructions on subscriber device 200preferably include multiple LBS or other mobile applications 203. Thelocal applications 203 may contact remote LBS or other applicationservers 101 to deliver the application-based information to thesubscriber. Subscriber device 200 further includes LBS agent 201. LBSagent 201 preferably manages or intermediates the communication oflocation LBS applications 203 with remote LBS applications 101 and/orgateway service 105. LBS agent 201 preferably forwards locationinformation to the appropriate LBS applications 101 and/or gatewayservice 105 at times defined by the respective applications. Also, LBSagent 201 may receive messages from applications 101 and forwards themessages to the respective local applications 203. LBS agent 201simplifies the implementation of LBS applications 203 and preventsconflict or difficulties in the execution of local LBS applications 203.Also, LBS agent 201 can manage updates to any LBS functionality that iscommon to all LBS applications 203 or one or several specific LBSapplications 203.

Subscriber device 200 further comprises software for presenting ads tosubscribers in an efficient manner. In one embodiment, subscriber device200 comprises ticker software application 204 and ad detail menuapplication 205. In preferred embodiments, some ads are communicated toLBS agent 201 of subscriber device 200 using SMS messaging. The SMSmessages preferably detail how the ad is to be presented to thesubscriber. Preferably, the SMS messages detail whether the ad is to beplaced into a ticker, for how long, and what particular text is to bedisplayed in the ticker. A ticker generally refers to a scrolling streamof characters on a screen of the wireless device (e.g., that mimics a“ticker-tape” in electronic form). LBS agent 201 provides theappropriate information to ticker software application 204 to display tothe user when the user reviews the screen of subscriber device 200(e.g., when the subscriber opens his/her phone). Also, the SMS messagespreferably detail information to be placed in a menu type form thatprovides a more detailed presentation of ads for subscriber review.Also, should a subscriber desire to view additional detail for an ad ordownload a digital coupon, a hyperlink can be included for userselection that causes browser application 206 to download thecorresponding content. In other embodiments, ads may be presenteddirectly within mobile apps. The mobile apps may also permit a user toclick on an ad to “click through” to more detailed ad presentation via abrowser application as an example.

In some embodiments, “digital coupons” are communicated to subscriberdevices 104 through the ad selection functionality of ad server 108 andad DB 107. The digital coupons are preferably implemented by use of adigital image encoded according to a digital rights management (DRM)scheme. The digital image can display the “coupon” details, such asproduct/store/location/purchase conditions, the amount of the coupon,etc. Also, the digital image preferably includes a “code” (e.g., analphanumeric string) that authenticates the validity of the coupon. Whena subscriber wishes to use a digital coupon, the user can present thescreen of the subscriber device 104 displaying the digital coupon to aclerk of a merchant. A merchant that has previously agreed to acceptsuch digital coupons can enter the code into the merchant's POS deviceduring a transaction. The merchant's system can then determine thevalidity of the coupon in real-time by communicating the code to asuitable server. Upon determining the validity of the coupon, themerchant's POS device can suitably adjust the transaction total. Also,the merchant's system can use the code to obtain settlement of thecoupon amount at a later appropriate time using the code.

The DRM functionality can be used for several purposes in the digitalcoupon process. In some embodiments, the DRM functionality ties thedigital coupon to a specific subscriber device 104, i.e., the digitalimage is not decrypted by other subscriber devices. Also, in someembodiments, location information can be encoded within the DRM rules.For example, spatial coordinates and a radius distance can be definedsuch that the digital image is only decrypted by the DRM software whenthe user is within the area defined by the spatial coordinates and theradius distance (to ensure that the coupon is only presented at desiredretail locations/merchants, etc.). That is, the DRM software accessesthe current location of the subscriber device 104 and selectivelydecrypts the digital image by comparing the current location to thelocation rules defined in the DRM license associated with the digitalcoupon.

In some embodiments, a short “ad” of several seconds (e.g., apromotional video) is incorporated with a digital coupon. When asubscriber initially reviews the digital coupon, the promotional videois played. After the promotional video is played, the digital imagecontaining the coupon information is then displayed. The DRM license cancontain a DRM rule that causes the video to be deleted upon review forthe purpose of minimizing the memory usage of the digital coupon overtime.

Some representative embodiments can provide a number of advantages. Forexample, by maintaining a database of sub-locations within specificstores and the types of goods at those sub-locations, intelligentselection of ads for delivery to subscribers can occur. For example, inordinary e-advertising and LBS advertising, it is most likely neveruseful to communicate an advertisement for an inexpensive, somewhatcommon-place food item. That is, the ad will have a very low probabilityof affecting the subscriber's purchasing activities. However, if it isknown that a subscriber is standing in a particular grocery aisle of a“big-box” retailer that contains that type of food item according torepresentative embodiments, communication of such an ad may makeeconomic sense because the probability of the ad being successful inaffecting the purchasing behavior is much higher than if the ad werecommunicated when the subscriber is at another type of location.

Additionally, by providing a log of activities, selection of ads forsubscribers can occur in a much more efficient and effective manner thatpossible according to conventional LBS applications. That is, subscriberactivities provide a more reasoned basis for estimating theappropriateness of an ad for a subscriber than the mere current locationof the subscriber. Also, by aggregating data over time and data frommultiple sources, the activities of a subscriber can be more accuratelyinferred. Also, by compiling historical norms, the effectiveness of anadvertisement in affecting immediate purchasing behavior can be morereadily determined.

FIGS. 7-14 depict activity norm summary information 700, 800, 900, 1000,1100, 1200, 1300, and 1400 that can be compiled or calculated for use inselecting ads to communicate to subscribers according to somerepresentative embodiments.

FIG. 7 depicts activity summary profile 700 according to onerepresentative embodiment. Activity summary profile 700 storesinformation that indicates the amount of time spent by a subscriber fora plurality of activities. Also, the information is provided in ahierarchical manner. Specifically, the amount of time is shown forvarious sub-activities. As shown in FIG. 7, for ACTIVITY 1, the averageamounts of time spent for ACTIVITY 1 per day of the week (Sunday throughSaturday) are represented by parameters V1-V7. The standard deviationsfor the amounts of time spent for ACTIVITY 1 per each day of the weekare represented by parameters s1-s7. The average amounts of time spentper week and per month for ACTIVITY 1 are represented by parameters V8and V9 and the standard deviations for time spent per week and per monthare represented by s8 and s9. In a similar manner, average amounts oftime and standard deviations are shown for SUBACTIVITIES, i.e., (V′1-V′9, s′ 1-s′9) for a first sublevel activity and (V″1-V″9, s″1-s″9)for a second sublevel activity. Any suitable number of subactivities andlevels of subactivities could be so provided. As shown in FIG. 7, thistype of information is repeated for a plurality of activities (through“ACTIVITY N”).

FIG. 8 depicts activity probability profile 800 according to onerepresentative embodiment. Activity probability profile 800 is definedfor one day of the week (i.e., Sunday). Preferably, similar profiles(not shown) are defined for other days of the week. Profile 800 definesthe probability that a subscriber will engage in a particular activitywithin a given time frame. For example, the probability that thesubscriber will engage in ACTIVITY 1 between 5 am and 6 am is defined bythe parameter P1. Likewise, the probabilities for the other hours of theday for ACTIVITY 1 are defined by parameters P2-P24. Probabilities forhierarchical subactivities are shown in parameters P25-P48 and P49-P72.Probabilities are preferably defined for a plurality of activitiesthrough ACTIVITY N.

FIG. 9 depicts shopping activity profile 900 according to onerepresentative embodiment. Profile 900 comprises relatively high levelshopping summary information. Profile 900 comprises the average amountsof time spent shopping per each day of the week, per week, and per monthin parameters X1-X9. The standard deviations for these times are shownin parameters x1-x9. The shopping frequencies for these time periods arerepresented by parameters F1-F9. The shopping frequency represents theaverage number of discrete shopping trips taken by the subscriber forthe respective time period. The standard deviations for the shoppingfrequency for these periods are represented by parameters f1-f9. Theaverage amounts of time per shopping trip are represented by parametersT1-T9 for these time periods with the standard deviations represented byparameters t1-t9.

The average amounts of time spent shopping per shopping location (e.g.,MALL X, MALL Y, . . . MALL Z, etc.) is shown for a plurality oflocations for these time periods. The locations are preferably retaillocations in which there are multiple merchant stores in relativelyclose proximity such as a mall or retail district. For LOCATION 1, theaverage amounts of time for the various time periods are represented byparameters L1-L9 with the standard deviations represented by parameters11-19. Also, the average numbers of stores visited by retail locationfor the time periods for LOCATION 1 are represented by parametersSL1-SL9 with the standard deviations represented by parameters s11-s19.Similar parameters are defined for a plurality of locations (as shownthrough LOCATION Z).

FIG. 10 depicts merchant type shopping profile 1000 according to onerepresentative embodiment. Profile 1000 preferably stores averageamounts of time spent shopping for a plurality of merchant types (e.g.,clothing retailers, electronics retailers, bookstores, big-boxretailers, grocery retailers, etc.). Also, the standard deviations aredefined (denoted by the “s” prefix). The amounts of time and standarddeviations are preferably calculated or compiled for each day of theweek, per week, and per month time periods. Also, average amounts oftime and standard deviations are defined for sub-store locations ordepartments for various merchant types. Such information is preferablycompiled or calculated for a plurality of merchant types (shown asMERCHANT TYPE 1 through MERCHANT TYPE X). FIG. 11 depicts merchantshopping profile 1100 according to one representative embodiment.Profile 1100 stores the same type of information as profile 1000 exceptthe information pertains to specific merchants as opposed to types ofmerchants.

When financial transactions are monitored and logged, shopping activitynorms in terms of purchases are preferably compiled or calculated. FIG.12 depicts shopping purchase profile 1200 according to onerepresentative embodiment. Profile 1200 stores average numbers ofpurchases per shopping trip (parameters NP1-1 through NP1-9) and averageamounts spent per shopping trip (parameters DP1-1 through DP1-9) foreach day of the week, per week, and per month time frames. The standarddeviations for these values are also given (parameters sNP1-1 throughsNP1-9 and sDP1-1 through sDP1-9). Average numbers and amounts ofpurchases for locations are defined (LOCATION 1 through LOCATION N) asshown in parameters NP-L1-1 through NP-L1-9 . . . NP-LN-1 throughNP-LN-9 and DP-L1-1 through DP-L1-9 . . . DP-LN-1 through DP-LN-9.Standard deviations are also defined for the locations for these timeframes as shown in parameters sNP-L1-1 through sNP-L1-9 . . . sNP-LN-1through sNP-LN-9 and sDP-L1-1 through sDP-L1-9 . . . sDP-LN-1 throughsDP-LN-9. Merchant type purchase profile 1300 (FIG. 13) and merchantpurchase profile 1400 (FIG. 14) depict similar purchase information(number of purchases, dollar amounts, standard deviations) by merchanttypes and specific merchants.

FIG. 15 depicts a flowchart for processing shopping activity informationfor a respective subscriber to generate profile information according toone representative embodiment. In 1501, activity information isretrieved for the last 6 months in a preferred embodiment (although anyother suitable length of time could be selected). The activityinformation is preferably retrieved from pre-existing activity logsaccording to one embodiment. Alternatively, location based informationcould be retrieved and correlated to activities in conjunction with thenorm building process. In 1502, total time is calculated for eachactivity (and subactivity) per day of week, per week, per month. In1503, the total amounts of time are divided by the numbers of each ofdays of the week, the number of weeks, and the number of months for theselected period of time. The values are stored in one or moreprofile(s).

In 1504, the number of times that each activity (subactivity) wasperformed in various time periods (e.g., each hour interval per day ofweek) is calculated. In 1505, the numbers of times for eachactivity/subactivity are divided by the total numbers of each of thedays of the week in the selected period of time and the resulting valuesare stored in one or more profile(s).

In 1506, the number of times that the subscriber went shopping over theselected period and for each day of week are calculated. In 1507, thecalculated numbers of times are divided by the number of months, thenumber of weeks, and the number of days, respectively. The resultingvalues are stored in one or more profile(s).

FIG. 16 depicts a flowchart for processing shopping activity informationfor a respective subscriber to generate profile information according toone representative embodiment. In 1601, activity log information isretrieved for last 6 months (or any other suitable period of time). In1602, the total amount of time spent shopping over the selected periodof time is calculated. Also, the total amount of time for the selectedperiod for each day of the week is also calculated. In 1603, thecalculated values are divided by the numbers of each day of the week,number of weeks, number of months in the selected period of time and theresulting values are stored in one or more profiles to generate theaverage amounts of time spent shopping per day of week, per week, andper month. In 1604, the standard deviations for the various time periodsare calculated and stored in one or more profiles.

In 1605, the calculation of averages and standard deviations for thetime is repeated for a plurality of shopping locations or respectiveretail locations in which multiple merchants are present. The calculatedvalues are stored in one or more profile(s). In 1606, the calculation ofaverages and standard deviations are repeated for each merchant type andsub-store location. The calculated values are stored in one or moreprofile(s). In 1607, the calculation of averages and standard deviationsare repeated for each merchant and sub-store location. The calculatedvalues are stored in one or more profile(s).

In 1606, the calculation of averages and standard deviations is repeatedfor each discrete shopping trip. That is, an individual shopping triprefers to a period of time where a subscriber was substantiallycontinuously engaged in a shopping activity. The average amounts of timespent shopping per shopping trip per each day of week, per week, and permonth are calculated and the standard deviations are calculated.

In 1607, the numbers of times that the subscriber went shopping over theselected period and for each day of week over the selected period arecalculated. In 1608, the calculated values are divided by the number ofmonths, the number of weeks, the number of each day of the week in theselected period of time and the resulting values are stored in one ormore store in one or more profiles.

FIG. 17 depicts a flowchart for processing financial activityinformation for a respective subscriber to generate profile informationaccording to one representative embodiment. In 1701, transactioninformation is retrieved for last 6 months or any other suitable periodof time. In 1702, the total numbers of purchases for each day of weekand total number of purchases are calculated. In 1703, the total dollaramounts of purchases are calculated for each day of the week and totaldollar amount of purchases over the selected period of time arecalculated. In 1704, the calculated values (from 1702 and 1703) aredivided by the numbers of each day of the week, the number of weeks, andthe number of months within the selected period to calculate the averagevalues to be stored in the one or more profile(s). In 1705, the standarddeviations for respective time periods are calculated and stored in oneor more profiles.

In 1706, the calculations are repeated to calculate the averages andstandard deviations for the various time periods for each shopping trip.The calculated values are stored in one or more profile(s). In 1707, thecalculations are repeated for each shopping location. The calculatedvalues are stored in one or more profile(s). In 1708, the calculationsare repeated for each merchant type. In 1709, the calculations arerepeated for each merchant. The calculated values are stored in one ormore profile(s).

FIGS. 18 and 19 depict example activity norm profiles 1800 and 1900 fordifferent types of merchants according to some representativeembodiments. Norm profiles 1800 and 1900 are preferably compiled bymonitoring activity information as determined using LBS data andfinancial information for the respective subscriber. Any number ofsimilar profiles can be defined for other types of shopping or spendingactivities. Preferably, some profiles are created and maintained foreach subscriber, although not every profile need be created andmaintained for each subscriber as some subscribers may not sufficientlyengage in the respective activity for the information to be useful.

Norm profile 1800 depicts activity norm data for “FAST FOOD DINING.”Norm profile 1800 depicts the percentage of times that the subscriberengages in the activity at the respective times (by breakfast, lunch,and dinner) for each day of the week and the average amount spent ateach respective time when the subscriber decides to engage in theactivity. It may be observed that at certain times the subscriber dineswith other parties, such as members of the subscriber's family, while atother times the subscriber dines alone (compare breakfast on Sunday withlunch on Wednesday). Profile 1800 further details percentages ofpurchases by restaurants and restaurants types. For example, profile1800 indicates that the subscriber dines at restaurant A 35% of timewhen the subscriber decides to engage in fast food dining. Profile 1800further indicates that the subscriber dines at a restaurant of type A45% of the time when the subscriber decides to engage in fast fooddining.

Norm profile 1800 preferably indicates other activities that arecorrelated to fast food dining. For example, norm profile 1800 indicatesthat when the subscriber is engaged in a “work—traveling” activity, thesubscriber engages in fast food dining 76% of the time (during orshortly thereafter the work-traveling activity). Also, norm profile 1800indicates that when the subscriber is or recently has engaging in a“shopping mall” activity, the subscriber engages in fast food dining 44%of the time (during or shortly thereafter the shopping mall activity).By providing such correlation information, specific ads can be directedto a subscriber at an appropriate time. Specifically, the ads might beable to reach the subscriber before the subscriber has made a decisionto engage in a specific activity or to go to a specific merchant. Thatis, if only current location data is utilized, “fast food dining” adsmight not be selected. Accordingly, the subscriber may make a decisionto engage at a specific fast food restaurant before ads are evercommunicated to the subscriber. Some embodiments potentially enable adsto be communicated to the subscriber at a relevant time but before thesubscribers has made such a decision. Thereby, the “steering” ability ofcommunicated ads according to some embodiments may be relatively high.

Norm profile 1900 is similar to norm profile 1800 except that normprofile 1900 includes norm parameters relevant to clothing shopping,clothing merchants, and clothing merchant types (e.g., young-women'sretailer, designer retailer, discount retailer, etc.). Profile 1900includes additional information. For example, norm profile 1900preferably includes a parameter that indicates that the number ofclothing-related purchases that the subscriber typically makes pershopping trip. Norm profile 1900 may also include information indicativeof the typical goods or type of goods purchased by the subscriber (e.g.,if the information is made available by the retailers in connection withcoupon, discount, or payment settlement processes).

Profile 1900 also preferably includes information that relates acorrelation between other financial considerations and the purchase ofclothing. Profile 1900 indicates that there is an increased probabilityof 50% of clothing purchases immediately after deposits into a financialaccount of the subscriber (e.g., when a paycheck or other funds aredeposited in the account). Also, there is an increase in the averageamount of said purchases immediately after deposits of 70%. It is seen,for this subscriber, that clothing purchases are highly correlated toavailable funds and, accordingly, the selection of ads for thissubscriber should also depend upon the deposit of funds into thesubscribers account (e.g., in terms of timing of the deposits and theamounts of the deposits).

Profile 1900 indicates decreased probability of 20% immediately afterout of budget expenses. Profile 1900 further indicates a decreasedaverage amount of purchase immediately after out of budget expenses of50%. In general, expenses may be categorized by analyzing the financialactivity of a subscriber to assign expenses/payments to variouscategories. See APPENDIX A of PCT Publication WO 2008/082794 A2.Significant deviations (e.g., greater than 20%, 30%, . . . 50%, or anysuitable dollar amount) from typical expenses for a significant budgetcategory may indicate that the subscriber is currently experiencingfinancial difficulty or unexpected expenses. For some subscribers, suchunexpected expenses may cause the subscribers to curtail certain otherpurchases. By correlating such unexpected expenses to purchasingbehavior, subscriber reaction to subsequent unexpected expenses may bepredicted and ad selection modified in response thereto. Accordingly,such information can be obtained and stored in subscriber profilesaccording to some representative embodiments for the purpose of adselection for subscribers.

FIG. 20 depicts activity norm analysis that cross-correlates selectedfinancial transaction behavior to other subscriber behavior. In 2001,deviations in typical expenditures (e.g., deviations exceeding 10%, 20%,30%, and 40% of typical discretionary or other spending) are identified.Such deviations may be performed to identify unexpected expenses orsignificant purchases that may impact other purchasing decisions of thesubscriber. In 2002, changes in purchasing behavior after identifieddeviations are identified for multiple shopping/spending categories interms of probabilities of purchases and amounts of purchases. In 2003,changes in probabilities and amounts of purchases are stored in profilesfor the various identified deviations (if any). In 2004, changes inpurchasing behavior after deposits into subscriber account(s) in termsof probabilities of purchases and amounts of purchases are analyzed In2005, changes in probabilities and amounts of purchases are stored inprofiles for various deviations. In 2006, changes in purchasing behaviorin relation to variation in amounts of deposits into subscriberaccount(s) in terms of probabilities of purchases and amounts ofpurchases are analyzed. In 2007, changes in probabilities and amounts ofpurchases are stored in profiles for various deviations.

FIG. 21 depicts a flowchart to identify correlations between purchasingbehavior of a subscriber and various activities performed by thesubscriber according to one representative embodiment. In 2101, anactivity and/or subactivity of subscriber is selected for analysis. In2102, occurrences of selected activity/subactivity in activity logs fora suitable time period (e.g., six months) are identified. In 2103, alogical comparison is made to determine whether the selectedactivity/subactivity has been performed greater than x number of timeswithin the period of time. If so, the process flow proceeds to 2104. Ifnot, the process flow proceeds to 2107.

In 2104, financial transactions within predetermined time period of eachoccurrence of activity/subactivity are retrieved (e.g., within one, two,or three hours, for example). In 2105, transaction types that haveoccurred at least y % of the time that the activity/subactivity wasperformed by subscriber are identified (e.g., clothing purchases,payments for dining, payments for various forms of entertainment, etc.).A suitable percentage of the time may be 50% according to one embodiment(although any other suitable percentage could be employed for otherembodiments). In 2106, identified transaction types and % for each typeof financial transaction are stored in one or more profile(s).

In 2107, a logical comparison is made to determine whether otheractivities/subactivities have been performed by the subscriber withinthe last six months or other selected time period. If so, the processflow returns to 2101 for selection of another activity/subactivity. Ifnot, the process flow ends at 2108.

In some embodiments, the information stored in DB 107 is utilized toanalyze and detect the collective activities of subscribers. In someembodiments, “clustering” of subscriber activity is detected. As used inthis application, clustering refers to multiple subscribers engaging ina common activity or activities within the relatively same geographicallocation. Clustering of such individuals could be detected over time byrepeatedly observing the close proximity in the locations of suchindividuals. That is, because the same subscribers are observed in veryclose physical proximity on multiple occasions, some type ofrelationship is believed to exist between such subscribers.Specifically, their repeated presence together is not a mere accident.Alternatively, the relationship between such subscribers could be knownusing a priori information (e.g., as provided by one or several of thesubscribers when opening an account with some web or other application,as defined by a social networking application, etc.). It shall beappreciated that clustering is not limited to any particular type ofrelationship. Clustering may occur in many contexts, e.g., familyactivities, gatherings of friends, business meetings, etc.

Clustering provides valuable insight into the expected behavior ofsubscribers and especially commercial behavior. Thus, the detection ofclustering provides a valuable mechanism to direct various types ofadvertising to such subscribers. The advertising may take the form ofdirect ads sent to wireless devices of the subscribers, e-mailadvertisements, web page advertisements, etc. The communication of adsmay occur while the clustering is taking place or may occur at a latertime. The communication may occur before the clustering takes place.That is, it may be possible to predict a clustering event (e.g., thespecific subscribers have been observed to cluster at the sameapproximate time/day, etc.) based on prior subscriber behavior. In sucha case, delivery of the ads may occur immediately before the estimatedtime of the predicted clustering event as an example.

As an example, a family may decide to go to a mall on a weekend day. Itis quite common for multiple members of the family to possess their owncellular phones. Perhaps, each parent and each teenager in the familywould possess their own cellular phone or other wireless device.Assuming that each family members' wireless device possesses a suitableLBS application that reports the respective subscriber's location to anLBS application or LBS gateway, the clustering of the family members canbe detected. For example, when the family members initially enter themall, the family members' respective GPS data may be very similar. Thatis, the LBS applications of their wireless devices may reportsubstantially similar location information. Also, as each family memberenters the mall, the GPS reception may fade at substantially the sametime (which can be communicated to an LBS application or gateway). Usingsuch close GPS or other location information, their very close proximityto each other can be detected thereby indicating that a clustering eventis occurring. Each member of the family may not necessarily be withinvery close proximity for the entire expedition to the mall. However,during the common activity, the activities of the family members willmost likely be inter-dependent in many ways even though the members arenot necessarily in very close proximity the entire time.

For example, the family members may initially separate to frequent eachfamily member's favorite stores. However, the family members may gatherback together to eat lunch or dinner together. Also, the purchases ofthe family members may be quite different when the family members aretogether as opposed to when the family members go shopping individually.For example, when a family is found to be clustering, purchasing may beskewed towards the children or teenagers of the family. If the parentsare found to cluster without the children, a different set of purchasingbehavior could be expected. Likewise, if each individual were determinedto be shopping alone, the purchasing behavior again may be different.Further, shopping in the context of peers or friends can exhibit anotherset of purchasing norms. Additionally, the individual making thepurchases may be different depending upon the presence of otherindividuals. For example, a parent may decide to go to a particularestablishment for a meal for the family which would not be chosen by anyindividual on their own. Hence, in such a situation, the type of ads formeals should depend upon whether the clustering is taking place and themembers of the current cluster. Also, it would be beneficial to identifythe party that is most likely to make the purchasing decision.

FIG. 3 depicts a flowchart according to one representative embodiment.In step 301, LBS information is accessed from one or several databasesfor prior locations of a selected subscriber as logged in thedatabase(s). In step 302, the database(s) is/are queried for othersubscribers that were present at substantially the same location as theselected subscriber at substantially the same time. In step 303, alogical comparison is made to determine whether there are one or moresubscribers that were repeatedly present at the same location as theselected subscriber.

If not, the selected subscriber has not been observed to exhibitclustering behavior and the process flow proceeds to step 304 whereanother logical determination is made. In step 304, it is determinedwhether there are additional subscribers to analyze. If not, the processflow proceeds to step 305 to quit. If there are, the process flowreturns to step 301 to select another subscriber.

If the logical comparison of step 303 determines that there are one ormore subscribers that were repeatedly present at the same location asthe selected subscriber, the process flow proceeds from step 303 to step306. In step 306, a suitable database update is completed to indicatethat the selected subscriber exhibits clustering activity. The databaseupdate may include indicating the identifiers of other subscribers withwhich the subscriber tends to cluster.

In step 307, the activities, associated financial transactions, etc.associated with the common locations are identified for the selectedsubscriber are identified. In step 308, one or more databases areupdated to indicate the type(s) of locations, type(s) of commonactivities and transaction data associated with the selectedsubscriber's clusters. The process flow proceeds from step 308 to step304 to determine whether there are additional subscribers for thecluster analysis process.

FIG. 4 depicts a flowchart for processing cluster data according to onerepresentative embodiment. In step 401, a cluster of subscribers(multiple subscribers that have been repeated observed within closeproximity of each other) is selected (e.g., as identified in one or moredatabases). In step 402, activity information and financial information(e.g., transaction details) for subscribers in the cluster areretrieved.

In step 403, the transactions by individuals in the cluster arecategorized (if not already so processed). In step 404, the member(s) ofthe cluster that are likely to pay for various transactions duringclustering are determined. In step 405, the types of goods and/orservices that exhibit an increased or decreased probability of purchaseare determined for the subscribers of the cluster. In step 406, thetypes of goods and/or services that exhibit a change in probability whenthe individuals are not clustering are identified. In step 407, thetypes of goods that exhibit a change in probability before and afterclustering are determined for the subscribers of the cluster. In step408, the activities that exhibit a change in probability (increase ordecrease) in conjunction with the clustering are identified.

In step 409, the information pertaining to the clustering is stored in asuitable database or databases.

FIG. 5 depicts a flowchart for utilizing cluster information accordingto one representative embodiment. In step 501, a request (e.g., an HTTPtransaction to a suitable LBS advertising web server application) isreceived from an LBS advertiser.

In step 502, a suitable web page is provided to the LBS advertiser thatpreferably includes interactive elements to enable the LBS advertiser toview subscriber information and to direct advertisements to suitablesubscribers. In step 503, cluster information is included within thesubscriber information for provision to the LBS advertiser. For example,the LBS subscriber may be allowed to click on a graphical element withinthe web page that represents a given subscriber. In response, subscriberinformation may be presented (e.g., an activity log, transactioninformation, activity norms, financial transaction norms, etc.). Withinsuch information, preferably the LBS advertiser is provided informationthat indicates whether the subscriber is current “clustering” and, ifso, with which other subscribers. The nature of the clustering ispreferably identified (e.g., family clustering, peer clustering,business clustering, etc.). Also, information that identifies the typesof transactions or activities that exhibit increased or decreasedprobability are preferably provided. By providing such information, theLBS advertiser can more effectively identify desirable subscribers forads and/or selected more appropriate ads for the subscribers.

FIG. 6 depicts another flowchart for utilizing cluster informationaccording to one representative embodiment. In step 601, advertisingparameters are received (which may include one or more clusteringparameter values). The advertising parameters define the desiredrecipients of one or more directed advertisements (e.g., as will bedelivered to subscriber wireless devices). For example, the followingtag-encoded parameters could be used as part of a desired LBSadvertising effort to direct advertisements to subscribers:{<LOCATION>STONEBRIAR MALL</LOCATION>AND <CLUSTERING>TRUE</CLUSTERING>AND (<CLUSTERINGWITHFAMILY>TRUE </CLUSTERINGWITHFAMILY}OR<CLUSTERINGWITHFRIENDS>TRUE </CLUSTERINGWITHFRIENDS>) AND(<CLUSTERINGPURCHASER>MEAL </CLUSTERINGPURCHASER>}. In this case, theads would be directed to subscribers located within or proximate to“Stonebriar Mall.” Also, the subscribers would be required to beclustering before the advertisement(s) associated with these parameterswould be delivered. Also, the subscribers would be required to beclustering with family members or friends (as opposed to businesspurpose clustering). Also, each advertising target would be required bythese parameters to be a subscriber within the respective cluster thattends to pay for meals during the clustering of the respectivesubscribers.

In step 602, ads are communicated by a suitable LBS advertising platformto subscribers according to the received parameters. The direction ofthe ads may directly depend upon the defined clustering parameters/dataprovided in the received parameters. For example, an advertiser maydirect that advertisements are only to be sent to members of a clusterthat make purchasing decisions for meals among other advertisingparameters in addition to providing non-clustering advertisingparameters. The ad parameters may be defined in terms of any of theclustering information discussed herein or any other suitable clusteringinformation. Alternatively, the advertiser may provide more generaladvertising parameters and automated subscriber selection algorithms canselect the most probable subscribers to respond based upon theclustering information.

Social network applications commonly refer to applications thatfacilitate interaction of individuals through various websites or otherInternet-based distribution of content. Originally, the concept of asocial network originated within the field of sociology as method ofmodeling social interactions or relationships. Within such modeling,individuals, groups, or organizations are represented as nodes within asocial network and the relationships between the “nodes” are representedas links between the nodes thereby forming a “network.”

Some known social network applications have (directly or indirectly)utilized such concepts to facilitate interaction between individuals viathe Internet. In most social network applications, a specific user cancreate an account and provide various types of content specific to theindividual, such as pictures of the individual, their friends, theirfamily, etc., personal information in text form, favorite music orvideos, etc. The content is then made available to other users of thesocial network application. For example, one or more web pages may bedefined for each user of the social network application that can beviewed by other users of the social network application. Also, socialnetwork applications typically allow a user to define a set of “friends,“contacts,” or “members” with whom the respective user wishes torepeatedly communicate. Users of a social network application may postcomments or other content to portions of each other's web pages.

For the purpose of this application, a social network application refersto any application or system (with communication over wired and/orwireless networks) in which users are permitted to create or defineaccounts in which the users can make personalized information andcontent available for viewing by other users of the social networkapplication and, in which, users can define, allow, or create contactsor friends within the social network application in which repeatedinteraction is intended to occur through the social network application.As used herein, an “account” of a social network application refers tothe collection of data maintained for a respective user for interactionwith the social network application and other users of the socialnetwork application whether stored together or separately. Thecollection of data may include user id, password, screen name, emailaddress, wireless device info., name information, demographicinformation, likes/dislikes, photos, activities, relationships, etc.

Some representative embodiments differ from certain previously knownsocial network applications. Some representative embodiments preferablyprovide functionality that enables users of a social network applicationto interact with other users or “friends” in unique ways. In someembodiments, users may interact with a mobile or mobile application on asmartphone of the user to indicate their “current place” forcommunication to other users or friends. Also, users may leave commentsfor other users for such places for presentation to their friends andusers (e.g., via their smartphones). As used herein, the term “mobileapplication” refers to an application on a mobile or wireless,subscriber device which conducts network communication using thewireless functionality of the subscriber device. Some representativeembodiments further provide functionality in a wireless phone to enablea user to upload data and/or images to the account of the user in anefficient manner for presentation to other users.

Some representative embodiments further enable users to indicate itemsto which the users have affinity or “like” via a “like function.” In yetfurther embodiments, the social network application includes locationbased service functionality as discussed herein. For example, activitiesof users of the social network application may be logged and employed todirect ads to users of the social network application (e.g., via amobile application or other software on a given user's smartphone).

Referring to system 2110 as shown FIG. 22, some embodiments may conductvarious social network application operations. Such operations mayinclude operating at least one social network application server 2115for interacting with users of the social network application. Thesoftware on server(s) 2115 may include a web server for serving pages ofthe social network application, e.g., HTML pages via HTTP protocols viaInternet 2113. Users on computers 2114 may access their accounts,uploaded data, communicate with friends, view other user web pages onthe social network application, etc.

In one embodiment, photo server is provided for direct uploading ofphotos to the social network application. Also, a mobile server may beprovided for facilitating interaction with wireless, telephonysubscriber devices 2111. The software on the application server maymaintain selected user account data (in database or other data store2116) for users of the social network application, where the useraccounts include data defining relationships between users of the socialnetwork application. The relationships may be referred to as “friends”providing a link between respective users of the social networkapplication. Such friends or users may have privileged status to viewcertain content posted to the account of another user. The content mayinclude photos (e.g., as stored in image DB 2117).

Preferably, at least some of the users of the social network applicationare users of wireless, telephony subscriber devices 2111 (whichcommunicate, at least partially, through wireless infrastructure 2112 ofa public, telephony network). Devices 2111 may also include otherwireless communication functionality (e.g., Wi-Fi or similar wirelessfunctionality). The social network application may provide software tooperate on wireless, telephony subscriber devices. The software may bein the form of browser-executable code or may be an application forinstallation and execution on the wireless, telephony subscriber device.

FIG. 23 depicts telephony device 2111 according to one representativeembodiment. Device 2111 may include conventional components such asprocessor 2205, wireless communication circuitry 2206, camera 2207, etc.Device 2111 may include memory 2204 for storing data and software. Thesoftware may include mobile browser 2201, social network mobileapplication 2203, and social network application picture uploadapplication 2202 according to some embodiments. Any other suitablesoftware may be included including software for communicating locationdata and/or device use data and software for receiving ads.

In some embodiments, a “like-function” is provided by the social networkapplication. The like-function is a function whereby a user of thesocial network application may input data, which is defined within thesocial network application and commonly understood by the users of thesocial network application, to indicate approval of or affinity with adefined item. That is, the user may input data via a user interface(e.g., on a smartphone app or via a web page on a smartphone orcomputer) to indicate such approval or affinity and the data is storedin the account of the user. The inputted data from the like-function maythen be shared with friends of the given user or other users. Thecommunication of “like-function” data to friends may be communicated tofriends based upon access control limits (e.g., all social networkusers, friends only, selected friends only, etc.).

The like-function may be implemented in any suitable form. For example,a simple boolean graphical user input (e.g., a radio button, a selectbutton, etc.) may be provided for the user to indicate the user'saffinity or approval. Alternatively, more complex user interface elementmay be additionally or alternatively employed, e.g., a text input boxfor specific comments. For commercial items, the like-function data maybe indicative of an item that the user is currently contemplatingbuying, the types of stores that the user is willing to purchase from,the price range of products that the user contemplates is appropriatefor the goods of interest, etc. Any such suitable like-function data maybe gathered in accordance with some embodiments.

In some embodiments, the like-function is combined with location data(e.g., as obtained by the smartphone of the respective user) to specifythe location of the item identified by the user via the like-function.The like-function data may also be communicated in a location dependentmanner, e.g., when selected friends are present at or arrive at alocation near or proximate to the location where the originating useridentified the item of interest.

Also, ads (e.g., as stored in ad DB 2119) may be selected based uponitems identified via the like-function and any other social networkapplication data discussed herein. The ads may also be communicated in alocation dependent manner. Any of selection algorithms discussed hereinmay be employed for direction of ads to users of the social networkapplication according to ad selection criteria for comparison againstlocation, analytic, and/or other data as stored in DB 2118 as anexample. Any suitable ad selection and distribution infrastructure maybe employed. For example, the multiple application infrastructurediscussed herein may also be employed according to some embodiments.

FIG. 25 depicts a user interface that may be provided by suitablesoftware on a wireless device 2111 (e.g., by mobile browser 2201 when itis executing suitable browser executable code or application 2203). Theinterface includes a button for the user to select to indicate affinityor approval of the defined item 2402. The defined item may be inputdirectly by the user via text entry. Alternatively, the defined item maybe pre-defined for selection by the user. The user may input appropriatecomments in text control 2403. In other embodiments, the user may alsoupload photos using control 2404. Control 2404 enables the user tobrowse files and file directories for selection of one or more photosfor the upload process. Button control 2405 enables the user to uploadthe photo(s) and data defined by the various controls.

In some embodiments, the social network application includesfunctionality to enable interaction and/or communication between usersof the social network application in relation to defined locations or“places.” In some embodiments, mobile app software is provided (e.g.,via downloading and/or installation) for operation on the wireless,telephony subscriber devices. The mobile app software is intended forinteracting with the social network application to access user accountsof the social network application. For example, the mobile app softwaremay enable a user to view the profile pages of the friends of the userand the profile pages of other users (as permitted by access controldata).

The mobile app software is preferably further adapted to enable a userof the wireless, telephony subscriber device to “check in” at respectivelocations from a plurality of locations. A “check in” refers to anoperation that is performed via the social network application (e.g.,through the mobile app software) to permit the user to indicate throughthe social network application the current whereabouts of the user. Thislocation may be displayed on the user's profile page, be communicated ona map interface provided by the social network application, or may becommunicated directly to the wireless, telephony subscriber devices offriends. The communication may occur in substantially real-time as theperformance of the check-in operation in certain embodiments.

In some embodiments, the check-in operation may identify a location froma plurality of locations that are defined in the social networkapplication and, preferably, includes places of business. The pluralityof locations may include user defined locations as directly input viathe software on subscriber devices.

In some embodiments, the mobile app software provides selections for theuser to control visibility of the user's current location (and/orrelated content) to other users of the social network including friendsof the user and customizable lesser subsets of all friends of the user.The user may select specific friends from a list to permit (or prohibit)viewing of the user's current location and location-content. Pre-definedgroups or lists of friends may be defined for selection for thispurpose.

In some embodiments, the mobile app software provides input capabilitiesto receive one or more comments to be automatically posted to theaccount of the user, the one or more comments being specifically tied torespective locations identified by functionality of the wireless,telephony subscriber device such that friends of the user as selectivelypermitted by the user are able to view the one or more comments of theuser on a map interface at the respective identified locations insubstantially real-time as the user who posted the one or more commentsis at the respective location.

In some embodiments, the number of check-ins are counted for specificmerchants for users of the social network application. Incentive offersare then communicated depending upon the specific number of check-inoperations performed by respective users. In this manner, users of thesocial network may be rewarded for loyalty to specific merchants and forparticipating in the check-in functionality of the social networkapplication.

Also as discussed herein in regard to operations of apps and wireless,telephony subscriber devices, activities of users of the wireless,telephony subscriber devices are preferably logged (e.g., as stored inDB 2118 and/or DB 2116 in FIG. 22) where the logged activities includedevice use, app use, and even real-world activities as discussed herein.These activities may be inferred from location data and/or device usedata. Alternatively, an individual user may explicitly input data toindicate the current activity of the user. The activities may bepresented for review by other users and/or may also be employed toselect advertisements for users of the social network application.

FIG. 24 depicts a user interface that may be provided by suitablesoftware on a wireless device 2111 (e.g., by mobile browser 2201 when itis executing suitable browser executable code or application 2203). Theinterface includes “check in” button, that when selected, causes thesoftware to communicate a suitable message to the social networkapplication that identifies the current location of the user for use bythe social network application. The location may be determinedautomatically using GPS functionality or other wireless locationalgorithms. Additionally or alternatively, the user may select alocation from list 2302. Additionally or alternatively, the user maydefine a location via interface for the check in operation. The user mayalso enter a comment via text control 2303 to be display with the user'scurrent location on the social network application. The visibility ofthe current location may be controlled using visibility list 2304 toselect all users, all friends, a lesser subset of friends (e.g., anumber of friends less than the full group of friends), or no visibilityat all as desired by the user.

In some embodiments, the social network application is adapted tofacilitate substantially real-time posting of photos to accounts (e.g.,for display via profile pages) taken directly from wireless, telephonysubscriber devices of users of the social network application.

In some embodiments, first mobile app software for operation on thewireless, telephony subscriber devices is provided for interacting withthe social network application to access user accounts of the socialnetwork application. The first mobile app software may be browserexecutable code (e.g., an HTML variant) or a mobile application. Secondmobile app software (e.g., software 2202 in FIG. 23) for operation onthe wireless, telephony subscriber devices is provided for postingphotos taken by the wireless, telephony subscriber devices to useraccounts of the social network application. The second mobile appsoftware is adapted to directly interact with camera functionality ofthe wireless, telephony subscriber devices to transfer a captured phototo at least one social network application server (preferably with photometadata, that is data entered via data input controls by the user of arespective wireless, telephony subscriber device).

The second mobile app software is preferably implemented to functionwithout requiring the respective user to log into the social networkapplication via the first mobile app software to view the user accountof the respective user. That is, the user can simply take a pictureusing the camera functionality of the wireless, telephony subscriberdevice and then post or transfer the photo to the user's account withoutrequiring the user to interact with the social network applicationthrough the conventional user account mechanisms (including navigatingto the user's login page, initial web page, selection of a photo tabfrom the initial web page, etc.).

On the server side of the social network application, messages from thesecond mobile app software are automatically parsed and the photos areautomatically posted to users accounts with data entered by therespective users as parsed from the messages (e.g., by photo serversoftware of server platform(s) 2105). Preferably (but not required), theautomatically parsing and automatically posting occurs through separateserver functionality than provided for web page access of user accountsof the social network application. The photos and messages becomeavailable for viewing on the social network application web pages of therespective users in substantially real-time.

In some embodiments, maps or map applications are provided in whichwireless, telephony subscriber device data is presented over a map of ageographical region (see, e.g., map interface 2600 in FIG. 26). Thedevice data may include subscriber analytic data. Alternatively, thedata may include specific subscriber information. The logged activities(current and past activities) of subscribers may be presented on themaps. Also, in some embodiments, subscribers are able to identifyspecific areas that are excluded from data gathering according tosubscriber privacy preferences.

In some embodiments, wireless device use data is received from multiplewireless, telephony subscriber devices by the at least one mobile deviceanalytic server. The received wireless device use data is processed togenerate subscriber analytic data and other data. Web queries arereceived from third-parties (e.g., other subscribers, advertisers, orother users). Web pages or web applications are communicated in responseto the received web queries according to an internet protocol to thethird-parties, including visual presentation of selected data items.

In some embodiments, each communicated web page or web applicationincludes a display of a map of a respective geographical region andsummary indications of wireless, telephony subscriber devices within therespective geographical region that are currently in use. The maps mayfurther display the summary indications for subscribers that matchsubscriber or device criteria specified in the corresponding web query.Also, the web page or applications may display summary analytic dataspecific for different mobile applications employed on multiple,wireless telephony subscriber devices or display specific summaryanalytic data for different types or groups of mobile applicationsemployed on multiple, wireless telephony subscriber devices. Forexample, all subscribers currently employing “gaming” applications or“social network” applications may be displayed on the map depending uponthe specific supplied criteria. Trending analytics may be employed(e.g., current activity or recent activity data compared to longer-termtypical or average analytic behavioral values).

When implemented in software, the various elements or components ofrepresentative embodiments are the code or software segments adapted toperform the respective tasks when executed on suitable computerhardware. The program or code segments can be stored in a machinereadable medium, such as a processor readable medium. The “computerreadable medium” may include any medium that can store or transferinformation. Examples of the computer readable medium include an randomaccess memory (RAM), electronic circuit, a semiconductor memory device,a ROM, a flash memory, an erasable programmable ROM (EPROM), a floppydiskette, a compact disk CD-ROM, an optical disk, a hard disk, a fiberoptic medium. The code segments may be downloaded via computer networkssuch as the Internet, Intranet, etc.

Although representative embodiments and advantages have been describedin detail, it should be understood that various changes, substitutionsand alterations can be made herein without departing from the spirit andscope of the appended claims. Moreover, the scope of the presentapplication is not intended to be limited to the particular embodimentsof the process, machine, manufacture, composition of matter, means,methods and steps described in the specification. As one of ordinaryskill in the art will readily appreciate from the disclosure thatprocesses, machines, manufacture, compositions of matter, means,methods, or steps, presently existing or later to be developed thatperform substantially the same function or achieve substantially thesame result as the corresponding embodiments described herein may beutilized. Accordingly, the appended claims are intended to includewithin their scope such processes, machines, manufacture, compositionsof matter, means, methods, or steps.

This application is related to (1) U.S. patent application Ser. No.12/463,168, May 8, 2009, which is a continuation of PCT applicationnumber PCT/2007/083987, filed 7-NOV.-2007 (published as WO 2008/082794A2) which claims priority to (i) U.S. patent application Ser. No.11/559,438, filed 14-NOV.-2006; (ii) U.S. patent application Ser. No.11/623,832, filed 17-JAN.-2007; (iii) U.S. Provisional PatentApplication Ser. No. 60/864,807, filed 08-NOV.-2006; and (iv) U.S.Provisional Patent Application Ser. No. 60/917,638, filed 11-MAY-2007and (2) is a related to U.S. patent application Ser. No. 12/967,040which is a continuation-in-part of U.S. patent application Ser. No.12/767,785, filed Apr. 26, 2010 which is a continuation-in-part U.S.patent application Ser. No. 11/747,286, filed May 11, 2007, which is acontinuation-in-part of U.S. patent application Ser. No. 11/623,832,filed Jan. 17, 2007, which is a continuation-in-part of U.S. patentapplication Ser. No. 11/559,438, filed Nov. 14, 2006 (which claims thebenefit of U.S. Provisional Application Ser. No. 60/736,252, filed Nov.14, 2005, U.S. Provisional Patent Application Ser. No. 60/759,303, filedJan. 17, 2006 and U.S. Provisional Patent Application Ser. No.60/773,852, filed Feb. 16, 2006); U.S. patent application Ser. No.11/623,832 also claims the benefit of U.S. Provisional PatentApplication Ser. No. 60/759,303, filed Jan. 17, 2006 and U.S.Provisional Patent Application Ser. No. 60/773,852, filed Feb. 16, 2006.All of the preceding applications are incorporated herein by reference

1. A method of sharing locations of users participating in a socialnetworking service at a geographic location, the method executed by acomputer system and comprising: receiving location information and textdescriptive information from a mobile device of a first user of thesocial networking service, the location information representing ageographic location of the first user, the text descriptive informationmanually provided by the first user on an input module of the mobiledevice; associating the location information with the text descriptiveinformation of the first user in a database; sending the textdescriptive information and the location information of the first userto a second user for display.
 2. A method of conducting social networkoperations for users participating in a social networking service, themethod executed by one or more computer systems and comprising:providing a mobile application for use on wireless computing devices ofusers for interaction with one or more servers of hardware and softwareof the social networking service, wherein the social networking serviceis adapted to permit a respective user to control a social networkingservice account to accept other following users to follow activities ofthe respective user via substantially real time messaging to wirelessdevices of the following users, wherein the mobile application isadapted to receive manually inputted text descriptive information from arespective user for sharing via the social network service forassociation with a user selected photo, wherein the mobile applicationis further operable to communicate geographic data indicative of acurrent location of the respective user during interaction with the oneor more servers of hardware and software of the social networkingservice; interacting, by the social networking service, with instancesof the mobile application on wireless computing devices of users of thesocial networking service to receive messages and location information,wherein the interacting comprises: automatically distributing messagesreceived from users using the mobile application to wireless devices offollower users identified in corresponding user accounts of the socialnetworking service in a substantially real time manner from receipt ofthe respective messages, wherein the distributed messages include photosand manually entered text descriptive information communicated from themobile application for display to following users; and providing asubstantially real-time ad matching service for providing ads to usersof the social networking service, wherein the ad-matching servicecontextually analyzes text descriptive information in messages fordistribution to following users for matching against ad parameters in adcampaigns managed via the ad matching service, wherein ads matched bythe ad matching service are communicated in conjunction withdistribution of corresponding messages to following users of the socialnetworking service.